This paper proposes a novel approach that detects and tracks carried objects by modelling the person-carried object relationship that is characteristic of the carry event. In order to detect a generic class of carried objects, we propose the use of geometric shape models, instead of using pre-trained object class models or solely relying on protrusions. In order to track the carried objects, we propose a novel optimization procedure that combines spatio-temporal consistency characteristic of the carry event, with conventional properties such as appearance and motion smoothness respectively. The proposed approach substantially outperforms a state-of-the-art approach on two challenging datasets PETS2006 and MINDSEYE2012. © 2013 Springer-Verlag.
CITATION STYLE
Tavanai, A., Sridhar, M., Gu, F., Cohn, A. G., & Hogg, D. C. (2013). Carried object detection and tracking using geometric shape models and spatio-temporal consistency. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7963 LNCS, pp. 223–233). https://doi.org/10.1007/978-3-642-39402-7_23
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